In this subsection, we validate the effect of MMD on improving recommender systems. Without loss of generality, we conduct our MMD on different recommender system models, such as User-based collaborative model (UBCF) [1]}, Item-based collaborative model (IBCF) [2]}, Matrix Factorization (MF-eALS) [3]}, [4]}, Bayesian personalized ranking (MF-BPR) [5]} and a state-of-the-art neural network-based model: neural collaborative filtering (NCF) [6]}. The details of these models can be found in the literature. And we tune the models carefully to achieve their best performance, respectively. To validate the effect of MMD, we fix all the parameters for all the models. The only difference is in terms of the input: the models input the datasets (rating matrices), which drop the 50 professional malicious users detected by MMD (with MMD), while the control models input the original datasets, which randomly drop 50 users (original). The input of RSs is only a rating matrix without reviews, splitting datasets as 60%, 20%, 20% as training, test, and validation.
